Update app.py

app.py

@@ -10,31 +10,6 @@ np.random.seed(0)
 
 
 
-def generate_plots(min_slider_samples_range,max_slider_samples_range):
-    # print("slider_samples_range:",slider_samples_range)
-      slider_samples_range =np.arange(min_slider_samples_range,max_slider_samples_range,1)
-      n_features = 100
-      repeat = 100
-      lw_mse = np.zeros((slider_samples_range.size, repeat))
-      oa_mse = np.zeros((slider_samples_range.size, repeat))
-      lw_shrinkage = np.zeros((slider_samples_range.size, repeat))
-      oa_shrinkage = np.zeros((slider_samples_range.size, repeat))
-      
-    
-      for i, n_samples in enumerate(slider_samples_range):
-          for j in range(repeat):
-              X = np.dot(np.random.normal(size=(n_samples, n_features)), coloring_matrix.T)
-    
-              lw = LedoitWolf(store_precision=False, assume_centered=True)
-              lw.fit(X)
-              lw_mse[i, j] = lw.error_norm(real_cov, scaling=False)
-              lw_shrinkage[i, j] = lw.shrinkage_
-    
-              oa = OAS(store_precision=False, assume_centered=True)
-              oa.fit(X)
-              oa_mse[i, j] = oa.error_norm(real_cov, scaling=False)
-              oa_shrinkage[i, j] = oa.shrinkage_
-        return
 
 
 
@@ -133,7 +108,31 @@ with gr.Blocks(title=title, theme=gr.themes.Default(font=[gr.themes.GoogleFont("
     # output = gr.Textbox(label="Output Box")
     # greet_btn.click(fn=greet, inputs=name, outputs=output)
     gr.Label(value="Comparison of Covariance Estimators")
-    generate_plots()
+    # generate_plots()
+    # print("slider_samples_range:",slider_samples_range)
+    slider_samples_range =np.arange(min_slider_samples_range,max_slider_samples_range,1)
+    n_features = 100
+    repeat = 100
+    lw_mse = np.zeros((slider_samples_range.size, repeat))
+    oa_mse = np.zeros((slider_samples_range.size, repeat))
+    lw_shrinkage = np.zeros((slider_samples_range.size, repeat))
+    oa_shrinkage = np.zeros((slider_samples_range.size, repeat))
+      
+    
+    for i, n_samples in enumerate(slider_samples_range):
+        for j in range(repeat):
+            
+            X = np.dot(np.random.normal(size=(n_samples, n_features)), coloring_matrix.T)
+    
+            lw = LedoitWolf(store_precision=False, assume_centered=True)
+            lw.fit(X)
+            lw_mse[i, j] = lw.error_norm(real_cov, scaling=False)
+            lw_shrinkage[i, j] = lw.shrinkage_
+    
+            oa = OAS(store_precision=False, assume_centered=True)
+            oa.fit(X)
+            oa_mse[i, j] = oa.error_norm(real_cov, scaling=False)
+            oa_shrinkage[i, j] = oa.shrinkage_
     #if min_slider_samples_range:
 
     min_slider_samples_range.change(plot_mse, outputs= gr.Plot() )